Can CNN autoencoders be improved by treating the output layer as an inverted hidden layer?
It seems like what you need is reinforcement learning method rather than AE / unsupervised. Method has to be like
Input -> CNN Feature Extractor (encode side of AE, i.e. VGG) -> Classifier (i.e. DNN) -> Choose one of the actions -> Observe change in the game and check if it was a wrong (like character is dying) or good (character is moving forward) and give a rating to action (feedback of decision). 

After a while, AI will learn how to jump over obstacles, avoid stuff etc.